Benchmarking Persistent Memory in Computers

Transcription

1 Benchmarking Persistent Memory in Computers Testing with MongoDB Presenter: Adam McPadden Co-Authors: Moshik Hershcovitch and Revital Eres August

2 Overview Objective Background System Configuration MongoDB Testing Results Conclusions August

3 Goals 1. Implement a Persistent Memory (PMEM) architecture in an Enterprise class POWER8 server running Linux 2. Measure application-level performance benefits of with MongoDB 3. Extrapolate benefits for Storage Class Memory (SCM) August

4 Background 2.5 DASD SuperCap card PCIe SuperCap card DRAM is mapped / host accessible DRAM data saved to FLASH upon power loss August

5 System Configuration Hardware FPGA 2x - 8GB Con Tutto FPGA Card S824L Power8 Server Memory Config: = 16GB, DRAM = 256GB August 2017 SSD: 400GB 6G SAS Note: Con Tutto FPGA contributes ~300ns to latency measurements 5

6 MongoDB - Definitions MongoDB: an Open Source, highly popular cross platform document-oriented NoSQL db Durability: the guarantee that written data has been saved Storage Engine (SE): Manages how data are stored to memory/disk MMAPv1: The original MongoDB SE (I/O s to disk for durability) : The standard SE from V3.2 (I/O s to disk for durability) In-Memory: Stores data in memory, does not guarantee persistence August

7 Objective The Challenge 1. Achieve the performance benefits of running in-memory 2. Guarantee durability / persistence 3. Lower write amplification Solution 1. Create a PMEM/SCM aware Storage Engine (IBM Research Haifa) 2. Use a fast, Non-volatile memory 3. Quantify MongoDB (YCSB) performance benefit with using existing Storage Engines using a PMEM aware Storage Engine August

8 SE Architecture The record store resides in a log structured array of segments in PM Indexes are implemented with a B-tree and stored in the volatile memory SE has DAX access to PM Insert a document MongoDB Volatile Memory Byte addressable persistent Memory Indexes Log structured memory Segments

9 Latency MongoDB: Latency 100% Writes MongoDB Latency Test = 100% Writes: Single Thread Load: 2M Writes Run: 2M Writes 74% Faster Net: Significant latency improvement with MMAPv1 SSD MMAPv1 SSD August

10 Latency MongoDB: Latency Mixed R/W MongoDB Latency 46% Faster Test = Mixed R/W: Single Thread Load: 3M Writes Run: 4M R/W 50/50 SE SSD Net: Significant latency improvement with August

11 LATENCY PMEM aware Storage Engine Storage Engine Latency 24% Faster Test = 100% Writes: Single Thread Load: 2M Writes Run: 2M Writes IBM-SE Net: Additional opportunity exists for a PMEM aware Storage Engine August

12 Latency Latency Latency MongoDB: SCM Extrapolation Memory Ratio 32:1 (DRAM:SCM) Memory Ratio 1:1 (DRAM:SCM) Memory Ratio 1:2 (DRAM:SCM) 5% Faster 25% Faster 30% Faster IBM-SE No Journaling IBM-SE No Journaling IBM-SE No Journaling Storage Class Memory Extrapolation Latency: + Con Tutto FPGA SCM Latency Domain (low hundreds of ns) Capacity: Modify the DRAM, Capacities to emulate different DRAM:SCM Ratios Results: Performance with a pmem aware Storage Engine increased with higher SCM:DRAM ratios. DBs which don t fit in DRAM cache, but can fit in larger SCM pmem space will be faster August

13 Conclusions Significant application level performance gains are achievable with and a PMEM architecture show appreciable latency benefits with existing MongoDB Storage Engines A Storage Class Memory/PMEM aware durable Storage Engine can further improve MongoDB performance Lower Write Amp: Journaling to disk is unnecessary Quantifying the end customer value proposition is critical in driving industry adoption of persistent memory with and Storage Class Memories August

14 Additional Resources OpenPOWER Contutto Presentation Programmable Near Memory Acceleration on Contutto Making Unforgettable MRAM Memory with OpenPOWER August